Self-Unlocking Active Clutch for Quasi-Passive Wearable Robots
Bibliographic record
Abstract
Wearable robots have gained attention as a promising technology for enhancing human functions and capabilities. While early research focused on developing motorized exoskeletons, recent efforts have shifted toward improving wearability for user convenience. However, the size and weight of actuators and battery components in active wearable robots remain significant challenges. As an alternative, passive wearable robots using nonmotorized mechanical components are lightweight and energy-efficient, but they have limitations in adapting to different situations. This article introduces a self-unlocking active clutch (SuAC) for quasi-passive wearable robots, which combines the benefits of both active and passive systems. The SuAC utilizes a shape memory alloy coil spring and an encoder to actively lock and provide assistive force based on the user's movement. Once in a locked state, the clutch can automatically unlock when the assistive force falls below a certain threshold, based on the user's preprogrammed intentions. This self-unlocking feature eliminates the need for additional mechanical triggering components or external sensors. The SuAC weighs approximately 50 grams and can withstand a locking torque of over 500 N, with a fast response time of less than 0.15 s. To demonstrate its application, we applied the SuAC to a neck-assist exosuit, showing that the assistive force can be controlled solely by the user's movements. This research simplifies the design and expands the functionality of quasi-passive wearable robots, providing a more accessible and efficient solution for assistive technology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".